Simulation of IR images of natural backgrounds

Ben−Yosef, N.; Rahat, B.; Feigin, G. D.

doi:10.1364/ao.22.000190

Cited by 29 publications

(5 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…There is a precondition that the non-parametric random field models are all based on the normal distribution. Simultaneously, the temperature field of the terrain can be regarded as a 2D normal Markov process [10] . We can convert the synthesized textures to temperature fields using a transformation by given the average temperature and the standard variation.…”

Section: Convert Texture To Temperature Fieldmentioning

confidence: 99%

Infrared texture simulation using non-parameteric random field model

Shao

Gong

2007

SPIE Proceedings

View full text Add to dashboard Cite

A non-parametric random field model by Ashikhmin algorithm is introduced to synthesize the infrared textures in detail. The non-parametric model provides the spatial structure of the infrared texture, which makes it more similar with the infrared textures taken by infrared camera. Then a conversion of texture to temperature field is given to yield a temperature distribution by given a group of temperature parameters. The Planck's radiation law is applied to obtain the emittance distribution sample, called physical infrared texture which is always used in the hardware-in-the-loop systems, from the temperature field. And a method to generate the appearance infrared texture is developed by applied the effect of infrared imaging system. Finally, some examples of infrared textures synthesized by Ashikhmin algorithm are presented in the paper. The simulation results show that the non-parametric model is more available, more efficient, more suitable and more easily controlled to synthesize the infrared textures in the infrared scenes simulations.

show abstract

Section: Convert Texture To Temperature Fieldmentioning

confidence: 99%

Infrared texture simulation using non-parameteric random field model

Shao

Gong

2007

SPIE Proceedings

View full text Add to dashboard Cite

show abstract

“…There are a number of different approaches to choose from when generating the height-data for ocean surface 8 . The Navier-Stokes Equations (NSE) is the cornerstone in the field of Fluid Mechanics and is defined by a set of differential equations that describe the motion of incompressible fluids.…”

Section: Geometry Modeling Of Ocean Surfacementioning

confidence: 99%

“…The normal field of ocean surface can be calculated by making derivative of equation (8). However, this method results in lots of work load.…”

Section: Generate Normal Map Of Ocean Surfacementioning

confidence: 99%

Real-time generation of infrared ocean scene based on GPU

et al. 2007

View full text Add to dashboard Cite

Infrared (IR) image synthesis for ocean scene has become more and more important nowadays, especially for remote sensing and military application. Although a number of works present ready-to-use simulations, those techniques cover only a few possible ways of water interacting with the environment. And the detail calculation of ocean temperature is rarely considered by previous investigators. With the advance of programmable features of graphic card, many algorithms previously limited to offline processing have become feasible for real-time usage. In this paper, we propose an efficient algorithm for real-time rendering of infrared ocean scene using the newest features of programmable graphics processors (GPU). It differs from previous works in three aspects: adaptive GPU-based ocean surface tessellation, sophisticated balance equation of thermal balance for ocean surface, and GPU-based rendering for infrared ocean scene. Finally some results of infrared image are shown, which are in good accordance with real images.

show abstract

“…[37] G(x, y) is generated in the Fourier plane, by the well-known device of representing the field as a Fourier series [Ben-Yosef et al, 1983;Bell, 1987]. To do so, the Fourier transform C(u, v) of the covariance function c G (x, y) is required.…”

Section: Stochastic Modeling Of Rainy Structures At Large Scalementioning

confidence: 99%

Large‐scale modeling of rain fields from a rain cell deterministic model

et al. 2006

View full text Add to dashboard Cite

A methodology to simulate two-dimensional rain rate fields at large scale (1000 Â 1000 km 2 , the scale of a satellite telecommunication beam or a terrestrial fixed broadband wireless access network) is proposed. It relies on a rain rate field cellular decomposition. At small scale ($20 Â 20 km 2), the rain field is split up into its macroscopic components, the rain cells, described by the Hybrid Cell (HYCELL) cellular model. At midscale ($150 Â 150 km 2), the rain field results from the conglomeration of rain cells modeled by HYCELL. To account for the rain cell spatial distribution at midscale, the latter is modeled by a doubly aggregative isotropic random walk, the optimal parameterization of which is derived from radar observations at midscale. The extension of the simulation area from the midscale to the large scale (1000 Â 1000 km 2) requires the modeling of the weather frontal area. The latter is first modeled by a Gaussian field with anisotropic covariance function. The Gaussian field is then turned into a binary field, giving the large-scale locations over which it is raining. This transformation requires the definition of the rain occupation rate over large-scale areas. Its probability distribution is determined from observations by the French operational radar network ARAMIS. The coupling with the rain field modeling at midscale is immediate whenever the large-scale field is split up into midscale subareas. The rain field thus generated accounts for the local CDF at each point, defining a structure spatially correlated at small scale, midscale, and large scale. It is then suggested that this approach be used by system designers to evaluate diversity gain, terrestrial path attenuation, or slant path attenuation for different azimuth and elevation angle directions.

show abstract

Simulation of IR images of natural backgrounds

Cited by 29 publications

References 0 publications

Infrared texture simulation using non-parameteric random field model

Infrared texture simulation using non-parameteric random field model

Real-time generation of infrared ocean scene based on GPU

Large‐scale modeling of rain fields from a rain cell deterministic model

Contact Info

Product

Resources

About